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1,838 results for “location”
Daily meteorological data (2000-2019) from PIE LTER weather stations located in Byfield/Newbury, MA
Meteorological data daily averages and daily fluxes for stations located at Governor's Academy and MBL Marshview Farm, Newbury, MA. Data includes air temeprature, precipitation, relative humidity, solar radiation, PAR, wind and air pressure measurements. Years 2000 to 2007 the station was located at Governor's Academy, Newbury, MA and was moved July 30, 2007 to the MBL Marshview Farm field station property where it is currently located.
AgMIP-Wheat multi-model ensemble simulations on climate change impact and adaptation for 60 representative global locations
<p>This is model output from the Agricultural Model Intercomparison and Improvement Project for wheat (AgMIP-Wheat) dataset of multi-model ensemble simulations for 60 representative global locations under different climate scenarios.</p> <p>The data have been generated following the modeling protocol of Asseng et al. (2019) and Liu et al. (2019).</p> <p>References</p> <p>Asseng, S. et al. (2019). Climate change impact and adaptation for wheat protein. Glob Chang Biol 25, 155-173, doi:10.1111/gcb.14481</p> <p>Liu, B. et al. (2019). Global wheat production with 1.5 and 2.0°C above pre-industrial warming. Global Change Biol 25, 1428-1444, doi:10.1111/gcb.14542</p> <p> </p>
Restart dataset for a single location in Norway ALP1 (61.0243N,8.12343E) for CTSM/FATES EMERALD Galaxy tutorial
<p>Restart files for CLM-FATES version 2.0.1 for <a href="https://github.com/NordicESMhub/ctsm/releases/tag/release-emerald-platform2.0.1">CLM-FATES EMERALD version 2.0.1</a>.</p> <p>CTSM_FATES-EMERALD_on_inputdata_version2.0.0_ALP1.tar_(restart_info):<br> - ALP1_refcase.datm.r.2300-01-01-00000.nc <br> - ALP1_refcase.datm.rs1.2300-01-01-00000.bin<br> - ALP1_refcase.cpl.r.2300-01-01-00000.nc <br> - ALP1_refcase.clm2.r.2300-01-01-00000.nc </p> <p>This dataset is being used in the <a href="https://training.galaxyproject.org/training-material/topics/climate/tutorials/fates/tutorial.html">Galaxy Training tutorial on CLM-FATES</a>.</p> <p> </p> <p>This work has been done in in collaboration with <a href="https://usegalaxy.eu/">Galaxy Europe</a> and <a href="https://www.eosc-life.eu/">EOSC-Life</a>:<br> - Within the 1st EOSC-Life Training Open Call, <a href="https://galaxyproject.eu/posts/2020/09/08/training-wp9-eosc-life/">two out of four proposals</a> have been awarded to the European Galaxy team to develop climate science e-learning material and mentoring and training opportunities for our communities.</p> <p>CLM-FATES documentation can be found <a href="https://fates-docs.readthedocs.io/en/latest/">here</a>.</p>
L'Aquila 2009 seismic sequence: integrated dataset of automatic first motion polarities focal mechanisms and RMT with HypoDD high quality relative earthquake locations
<p>This dataset is related to the L'Aquila 2009 seismic sequence that happened in Central Apennines (Italy).</p> <p>It contains:</p> <ul> <li>2782 quality selected focal mechanisms produced with the standard software FPFIT based on automatically determined first motion polarities of automatically detected and analyzed foreshocks and aftershocks recorded from January 2009 to December 2009 (flag <strong>fty</strong> in the header is MP)</li> <li>475 (out of 627) quality selected focal mechanisms produced with the standard software FPFIT also based on automatically determined first motion polarities but for only 3204 M<sub>L</sub> >= 1.9 earthquakes and by using take-off angles calculated within a local 3d tomographic velocity model (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2011GL047365">Di Stefano et al., 2011</a>) , published and released in <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2011JB008352">Chiaraluce et al., 2011</a> (flag <strong>fty</strong> in the header is JG)</li> <li>165 (out of 181) Regional Moment Tensors determined for earthquakes M<sub>L</sub> >= 3.0 based on broadband waveform inversion of ground velocities and published by <a href="https://pubs.geoscienceworld.org/ssa/bssa/article-abstract/101/3/975/349796/Regional-Moment-Tensors-of-the-2009-L-Aquila">Hermann et al., 2011</a> (flag <strong>fty</strong> in the header is HM)</li> <li>The hypocenters of the total 3422 earthquakes reported in the present focal solutions dataset have been taken from the very high quality double difference locations of the about 64000 aftershocks reported in <a href="https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgrb.50130">Valoroso et al., 2013</a> and published, as part of the full dataset, <a href="https://doi.org/10.5281/zenodo.4036248">on Zenodo</a>. </li> </ul> <p>The association between the focal solutions and the HypoDD hypocenters has been performed through the direct use of the HypoDD event identifier where possible (the whole MP dataset) and through spatial and temporal earthquakes coordinates matching in all the other case by using the capability of a MySQL database. </p> <p>Two files are uploaded, one in plain text with blank separator, the second in plain text with ";" separator and .csv extension.</p> <p>Here below the header is explained.</p> <p><strong>OT_Date:</strong> date of the origin time in the format YYYY-MM-DD</p> <p><strong>OT_Time:</strong> time of the origin time in the format HH:mm:ss.dcm</p> <p><strong>lat:</strong> hypocenter latitude expressed in degrees </p> <p><strong>lon:</strong> hypocenter longitude east of Greenwich, expressed in degrees</p> <p><strong>dep:</strong> hypocenter depth expressed in km </p> <p><strong>ML:</strong> local magnitude (pure number) from <a href="https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgrb.50130">Valoroso et al., 2013</a> (see last column notes also)</p> <p> </p> <p><strong>id_dd:</strong> the hypoDD event identifier, allowing to directly connect to the <a href="https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgrb.50130">Valoroso et al., 2013</a> full dataset</p> <p><strong>IMPORTANT NOTE about st1 and st2 (below): </strong>the focal solutions are presented here based on the convention they where produced or published, so there are two different (but compatible) conventions for the fault plains orientation in the 3d space</p> <p><strong>st1:</strong></p> <ul> <li><strong>for fty=</strong>HM or JG this is the strike of plane 1 (CMT convention)</li> <li><strong>for fty=</strong>MP this is the <strong>strike of the dip direction </strong>of plain 1 (FPFIT convention)</li> </ul> <p><strong>dip1: </strong>dip of plane 1</p> <p><strong>rk1: </strong>rake of plane 1</p> <p><strong>st2:</strong></p> <ul> <li><strong>for fty=</strong>HM or JG this is the strike of plane 2 (CMT convention)</li> <li><strong>for fty=</strong>MP this is the <strong>strike of the dip direction </strong>of plain 2 (FPFIT convention)</li> </ul> <p><strong>dip2: </strong>dip of plane 2</p> <p><strong>rk2: </strong>rake of plane 2</p> <p><strong>fty:</strong> flag to distinguish the type of solution, CMT=HM or JG, FPFIT=MP</p> <p><strong>MW:</strong> only for HM, this columns reports also MW from <a href="https://pubs.geoscienceworld.org/ssa/bssa/article-abstract/101/3/975/349796/Regional-Moment-Tensors-of-the-2009-L-Aquila">Hermann et al., 2011</a></p>
Building locations in Poland in 1970s and 1980s
<p>Dataset contains building locations in Poland in 1970-80s. The source information were polish archival 1:10 000 topographical maps. Buildings were extracted from maps using Mask R-CNN model implemented in Esri ArcGIS Pro software. In post processing we have removed most of the false possitives. The dataset of building locations covers the entire country and contains approximately 11 million buildings. The accuracy of the dataset was assessed manually on randomly selected map sheets. The overall accuracy is 95% (F1 0.98).</p>
Shapefiles showing the locations of long-term climate change refugia and hotspots identified in the FairSeas report "A Climate Resilient Path for Ireland's Marine Protected Areas Network"
<p>Shapefiles created for the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network", an addendum chapter to "Revitalising Our Seas report: Identifying<br>Areas of Interest for Marine Protected Area Designation in Irish Waters"</p> <p>These shapefiles summarise long-term patterns that emerge from the spatial-meta analysis of physical-biogeochemical and species distribution modelling data, providing an overview of the distribution of climate change refugia and climate change hotspots across Ireland's National Marine Planning Framework between 2026 - 2069, and across the two emissions scenarios considered in the report (RCP4.5 and RCP8.5). </p> <p>Filenames refer to the specific analysis each set of shapefiles belong to: Benthic habitats, benthic megafauna, pelagic habitats, pelagic megafauna and forage fish. Details of the modelling datasets used in each of these analyses, the meta-analysis method and shapefile creation can be found in Annex A1 in the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network".</p>
Madrid Grid Area Buildings + Reachable Endpoints for Given RF Transmitter Location and Parameters, with and without RISs Installation.
<p>1- The obstacles_save folder contains arrays defining the vertices locations (x,y) of buildings in the considered area in Madrid Grid.</p> <p>A transmitter is placed at the center of a square at location [600, 900]. Possible receiver (or relay trasnceivers) locations are defined as the vertices (i.e., corners) of buildings (from previous list). The goal of the simulation is to find how many hops are needed to reach, if possible, each location from the previously mentioned list of vertices, assuming a maximum allowed path loss value of 90 dB between any two consecutive hops. </p> <p>2- The arrays in no_ris specify the vertices reachable within N sucessive hops, when no RIS is installed in the area.</p> <p>3- Similarly, the arrays in double_ris give the coordinates of vertices reachable with N hops when a two RISs are installed in the middle square(as shown in related paper).</p> <p>The RIS beamforming gain is 20 dB (in Table 1 in the paper the gain should be 20 not 15 dB).</p>
MTAB3D: a 3-D velocity model for absolute hypocenter location in southern Iberia and westernmost Mediterranean.
<p>The Trans-Alboran Shear Zone is one of the most seismically active areas in the westernmost Mediterranean, where a wide variety of tectonic domains have developed within the context of oblique convergence between Eurasia and Africa plates. In this region, earthquakes occur close to seismogenic structures, some of them large enough to cause damaging events. In addition, the diversity of tectonic domains implies a lateral variation of seismic wave propagation, which could affect the hypocenter reliability if not addressed during the location procedure. In this work, we present mTAB3D, a new 3D P-wave velocity model that accounts for the lateral heterogeneity of our study area. The new catalogs computed with our model help us to infer possible genetic relations between seismicity and source faults within our study area and can be used as an additional tool when looking into prior seismic sequences.</p>
The location of solar farms within England's ecological landscape: implications for biodiversity conservation
<p>Data associated to the article entitled 'The location of solar farms within England's ecological landscape: implications for biodiversity conservation'. </p> <p> </p>
Plausible 2050 offshore wind locations in the North Sea
<p>This dataset contains a set of zones and points representing plausible locations for offshore wind farms and individual turbines to have been built in the North Sea by the years 2030, 2040, and 2050, based off the national ambitions announced up to summer 2024.</p> <p>This version (version 3) is a major revision. Many wind farm zones and most turbines have moved. Column names have changed. See readme.pdf for further information and a changelog.</p> <p>A full description of how these coordinates were arrived at is currently under development as a journal article, and once it is available this readme will be updated to link to it. Check the "latest version" link in Zenodo to see if this has already happened.</p> <p>If using this version, please cite the dataset directly using the title and authors above and DOI 10.5281/zenodo.14222865</p> <p>Please do not use "OSW zones.png" for serious work; use the underlying data instead. The image is included so as to give a useful preview in Zenodo.</p>
Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment - Measurements and Locations
<p>Many wetlands in East Africa are farmed and wetland reservoirs are used for irrigation, livestock, and fishing. Water quality and agriculture have a mutual influence on each other. Turbidity is a principal indicator of water quality and can be used for, otherwise, unmonitored water sources. Low-cost turbidity sensors improve in situ coverage and enable community engagement. The availability of high spatial resolution satellite images from the Sentinel-2 multispectral instrument and of bio-optical models, such as the Case 2 Regional CoastColor (C2RCC) processor, has fostered turbidity modeling. However, these models need local adjustment, and the quality of low-cost sensor measurements is debated. We tested the combination of both technologies to monitor turbidity in small wetland reservoirs in Kenya. We sampled ten reservoirs with low-cost sensors and a turbidimeter during five Sentinel-2 overpasses. Low-cost sensor calibration resulted in an R² of 0.71. The models using the C2RCC C2X-COMPLEX (C2XC) neural nets with turbidimeter measurements (R² = 0.83) and with low-cost measurements (R² = 0.62) performed better than the turbidimeter-based C2X model. The C2XC models showed similar patterns for a one-year time series, particularly around the turbidity limit set by Kenyan authorities. This shows that both the data from the commercial turbidimeter and the low-cost sensor setup, despite sensor uncertainties, could be used to validate the applicability of C2RCC in the study area, select the better-performing neural nets, and adapt the model to the study site. We conclude that combined monitoring with low-cost sensors and remote sensing can support wetland and water management while strengthening community-centered approaches.</p> <p>The provided dataset includes a point shapefile with the studied reservoirs in central Kenya and a data table with the sampling date (Sentinel-2 overpass plus/minus one day), low-cost sensor setup number, reservoir ID, sampling location within the reservoir, the voltage measurements of the three respective low-cost sensor heads for sensor setups A and B, the averaged voltage, and the turbidimeter measured turbidity value in nephelometric turbidity units (NTU).</p> <p>The study is available in (please cite):</p> <div> <div>Steinbach, S., Rienow, A., Chege, M.W., Dedring, N., Kipkemboi, W., Thiong’o, B.K., Zwart, S.J., Nelson, A., 2024. Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment. <em>IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing</em> <em>17</em>, 8490–8508. <a href="https://doi.org/10.1109/JSTARS.2024.3381756">https://doi.org/10.1109/JSTARS.2024.3381756</a></div> </div> <p>This research was supported in part by the German Federal Ministry of Education and Research (BMBF) through the Project “Participatory Approach to Environmental Conservation of the Muringato Catchment Area for Sustainable Management and Enhanced Ecosystem Health” (CITGI4Muringato) under Grant Agreement No. 01DG20022.</p>
The World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series
<p><strong>The World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series</strong></p> <p>This repository contains global lightning stroke density and stroke power calculated from georeferenced stroke count data from the World Wide Lightning Location Network <a href="http://wwlln.net">WWLLN</a>. The real-time raw stroke count data were reprocessed by WWLLN to remove artifacts and improve geolocation, which resulted in the "AE" georeferenced and timestamped stroke count data. These data were then gridded at 0.5 degree 5 arc-minute and hourly resolution, converted into density, and corrected for detection efficiency using the WWLLN global gridded detection efficiency maps. Mean, median, and standard deviation of stroke power are also provided at 30-minute resolution. The corrected hourly rasters were then aggregated into daily and monthly totals and into a multi-year monthly mean climatology. The data cover the period 2010-2024 and will be updated in the coming years.</p> <p>For a complete description of the data see:</p> <p>Kaplan, J. O., & Lau, K. H.-K. (2021). The WGLC global gridded lightning climatology and time series. <em>Earth System Science Data, 13</em>(7), 3219-3237. <a href="dx.doi.org/10.5194/essd-13-3219-2021">doi:10.5194/essd-13-3219-2021</a></p> <p>Kaplan, J. O., & Lau, K. H.-K. (2022). World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series, 2022 update. <em>Earth System Science Data, 14</em>(12), 5665-5670. <a href="dx.doi.org/10.5194/essd-14-5665-2022">doi:10.5194/essd-14-5665-2022</a></p> <p>The data are stored in a <a href="https://www.unidata.ucar.edu/software/netcdf/">NetCDF</a> (version 4) files and have the following attributes:</p> <ul> <li>Spatial extent: Entire Earth</li> <li>Spatial reference system (SRS): Unprojected (geographic, WGS84)</li> <li>Spatial resolution: half-degree and 5 arc-minute</li> <li>Temporal extent: 2010-2024</li> <li>Temporal resolution: daily and monthly*1,2</li> </ul> <p><strong>Variables included in this release</strong></p> <ul> <li>Lightning density (strokes km-2 day-1)</li> <li>Lightning mean, median, and standard deviation of stroke power (MW, 30 arc-minute version only)</li> </ul> <p>For further details, see <a href="https://github.com/ARVE-Research/WGLC">https://github.com/ARVE-Research/WGLC</a></p> <p>1*5479 elements in the time dimension for daily data; 180 for monthly data; 12 for the climatology.</p> <p>2*Daily fields currently available at 30-minute resolution only.</p> <p><a href="../doi/10.5281/zenodo.4774528">The WWLLN Global Lightning Climatology and timeseries (WGLC) </a>© 2025 by Jed O. Kaplan is licensed under <a href="http://creativecommons.org/licenses/by-sa/4.0/?ref=chooser-v1">CC BY-SA 4.0</a></p>
Towards new demography proxies and regional chronologies: Radiocarbon dates from archaeological contexts located in the Czech Republic covering the period between 10,000 BC and AD 1250 (dataset)
<p>The dataset was created within the project “<em>Land use, social transformations and woodland in Central European Prehistory. Modelling approaches to human-environment interactions</em>” funded by the Czech Science Foundation (19-20970Y). This dataset represents the largest and the most comprehensive collection of archaeological radiocarbon dates from the Czech Republic to date. The dataset offers 1579 samples from 347 archaeological sites dating from Early Mesolithic (10 000 BC) to Medieval Period (AD 1250). Published in a simple spreadsheet format, the database offers researchers a quick tool for further analyses. It is important to highlight that dates we collected originated only from archaeological contexts, which means that we have excluded some radiocarbon dates produced through palaeoecological research without a direct relationship to past human activities, such as pollen records or samples from fossilized trees in river beds. The dataset is intended to be used for demographic modelling of population numbers during periods without written records, i.e. prehistory.</p>
Recognized Fault Locations and Attributes, Getaberget, Åland Islands
<p>We mapped faults using their secondary indicators such as damage zones and secondary fracturing at Getaberget shoreline, Åland Islands, Finland.</p> <p>Coordinates are in x and y -columns in<em> EPSG:3067 ETRS-TM35FIN</em> coordinate system.</p> <p>The field mapping and photos were taken as part of a Geological Survey of<br> Finland project, KYT KARIKKO, with funding from Finnish National Nuclear Waste<br> Management Fund (KYT) during the summers of 2020 and 2021.</p>
Paired Sentinel-1 and Sentinel-2 Images for 2 Locations in Scotland and India for 2019 and 2020
<p>The dataset contains two years of coverage (2019 and 2020) for two distant geographical areas in India and in Scotland.</p> <p>If using this dataset, please cite the paper where it has been introduced:</p> <pre><code>@article{rs14061342, author = {Czerkawski, Mikolaj and Upadhyay, Priti and Davison, Christopher and Werkmeister, Astrid and Cardona, Javier and Atkinson, Robert and Michie, Craig and Andonovic, Ivan and Macdonald, Malcolm and Tachtatzis, Christos}, title = {Deep Internal Learning for Inpainting of Cloud-Affected Regions in Satellite Imagery}, journal = {Remote Sensing}, volume = {14}, year = {2022}, number = {6}, article-number = {1342}, url = {https://www.mdpi.com/2072-4292/14/6/1342}, ISSN = {2072-4292}, DOI = {10.3390/rs14061342} }</code></pre> <p> </p>
Fine-scale population spatialization data of China in 2018 based on real location-based big data
<p><strong>This data contains a geospatial population raster layer in GeoTIFF format with 1*1 km resolution for 31 provincial regions (2851 counties) of China in 2018 (pop2018.tif). It also provides the Tencent positioning data in 2018 (TN_hSum2018.tif), the table of statistical population of 2851 counties (statistical_population_2018_china_county.xls) and its vector map (statisitcal_pop.shp) and codes (code.docx).</strong></p>
Location, biophysical and agronomic parameters for croplands in Northern Ghana
<p>We present a dataset describing (i) crop locations, (ii) biophysical parameters and (iii) crop yield and biomass was collected in 2020 and 2021 in Ghana, mostly focusing on maize in northern Ghana. The dataset contains repeated multiple measurements of leaf area index (LAI), leaf chlorophyll concentration over a large number of maize fields, as well as associated grain yield, biomass and polygons that delineate the fields.</p>
Location Decision
<p>This dataset gathers the replies of a conjoint experiment conducted online on 1'596 respondents leading to 15'960 cases in total.<br> The data assess what drives immigrants' intentions as regards residential location choice and, thus, comprise different factors playing a role in residential location choice (e.g. political factors, access to nature, living costs, public transport, facilities).</p> <p>Each respondent had to decide in which commune s/he would better settle in. In total, 5 vignettes comparing two municipalities were presented to respondent.</p>
Climate-representative locations according to the ASHRAE 169-2020 climate classification and within the WMO Region VI (Europe)
<p>This dataset comprises climate-representative locations according to the ASHRAE 169-2020 climate classification and within the WMO Region VI (Europe). This is, for each climate zone of the ASHRAE 169-2020 standard within the WMO Region VI (Europe), one location in close agreement with its centroid was selected based on the classification criteria. The recent typical meteorological year (TMYx.2007-2021 or TMYx.2004-2018) for each location is freely provided by Climate.One.Building.Org repository (https://climate.onebuilding.org/).</p>
ALS-based DEMs (100 cm): the dynamics of the prehistoric communities located in the Mostiștea Valley and Danube Plain (between Oltenița and Călărași)
<p>This dataset is part of a larger project on the dynamics of the prehistoric communities located in the Mostiștea Valley and Danube Plain (between Oltenița and Călărași), supervised by the ArchaeoSciences Division of the Research Institute of the University of Bucharest (ICUB) and Kiel University (Germany), in partnership with HOGENT, University of Applied Sciences and Arts (Belgium), Museum of Bucharest, Museum of the Lower Danube Călărași, Museum of Gumelnița Civilization Oltenița, and "Vasile Pârvan" Institute of Archaeology (Romania), under the "Sultana School of Archaeology" initiative.</p> <p>Spatial data play a crucial role in archaeological research, and orthophotos, digital elevation models, and 3D models are frequently used for the mapping, documentation, and monitoring of archaeological sites. Thanks to the availability of compact and low-cost uncrewed airborne vehicles, the use of UAV-based photogrammetry is well matured in this field over the last two decades. More recently, compact airborne systems are also available that allow the recording of thermal data, multispectral data, and airborne laser scanning. For this project, various platforms and sensors are applied at the Chalcolithic archaeological sites in the Mostiștea Basin and Danube Valley (Southern Romania). By analyzing the performance of the systems and the resulting data, insight is given into the selection of the appropriate system for the right application. This analysis requires thorough knowledge of data acquisition and data processing as well. As both laser scanning and photogrammetry typically result in very large amounts of data, a special focus is also required on the storage and publication of the data. Hence, the objective of this project is to provide a full overview of various aspects of 3D data acquisition for UAV-based mapping. Based on the conclusions drawn in our related publications, it is stated that photogrammetry and laser scanning can result in data with similar geometrical properties when acquisition parameters are appropriately set. On the one hand, however, the used ALS-based system outperforms the photogrammetric platforms in terms of operational time and the area covered. On the other hand, conventional photogrammetry provides flexibility that might be required for very low-altitude flights, or emergency mapping. Furthermore, as the used ALS sensor only provides a geometrical representation of the topography, photogrammetric sensors are still required to obtain true color- or false color composites of the surface. Lastly, the variety of data, like pre- and post-rendered raster data, 3D models, and point clouds, requires the implementation of multiple methods for the online publication of data. Various client-side and server-side solutions are presented to make the data available for other researchers.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.